The increased reliance on data assets has made the ability to run reports, generate statistics and generally manipulate quantities of databases and other data resources a significant advantage for corporations, governments and other entities. However, much of the customer, transaction and other types of data warehoused by companies and others is stored in formats which were chosen long ago, which might not be compatible with current standards or which may not be easily modified to increase functionality. These drawbacks may hinder an organization's ability to mine its databases for strategic trends or other purposes.
For instance, in an example referred to as Example 1, as illustrated in FIG. 3, a retail organization may maintain a database with separate records for corporate Sales and Budget. The organization may market goods grouped into categories, such as clothes or furnishings, with individual brands or items populating each category. In the illustrated data schema, the Sales record may be set up with fields or keys for day (date), items and stores, whereas the Budget record may be configured with month, category and store fields. Suppose that the organization would like to compare its budget for a given day of the year against the actual sales for that date.
According to the database structure shown in FIG. 3, it is impossible to make that comparison or run that report for a selected date without some complications. In many situations, additional logic must be created to generate a correct report. The search logic of most database platforms requires that a common field be found to create a match and generate an output based on that key field search. The Budget record however only contains a field for the budget on a monthly, rather than daily, basis and no match may be made on daily sales or totals between the two illustrated records.
Moreover, searches, comparisons or reports may not be able to be run between two separate databases, or the records therein, if some schema commonality can not be found. This reduces the analytic effectiveness of data resources.
Other problems exist.